IKEA furniture embodies many elements of good cabinet making but skips many nonessential elements. And does this more consistently than cabinet makers who can be bored, incompetent, depressed, burnt out, resentful, tired, having a bad day.
In the future AI code inevitably will embody most good software engineering practices. And will do this more consistently than software engineers who can be bored, incompetent, depressed, burnt out, resentful, tired, having a bad day.
Just look at the messages on HN or around you at your colleagues to see how mediocre the average software engineer is..
Today's IKEA is good enough for most people.
Tomorrow's AI coding will be good enough for most corporations.
Good enough to vastly reduce the need for fine craftsmen and women / software engineers.
Good enough to deskill those who call themselves cabinet makers / senior software engineers. These days the cabinet makers I personally know just do contract kitchens for project builders.
But IKEA is and AI will be, bad enough that at the high end with special requirements / taste / money / an inflated sense of self worth, some furniture makers still exist and thrive.
Perhaps 1% percent of current software engineers of today will be needed in the future when AI code inevitably has the ability to follow good software engineering practice.......
And as usual it will mainly be the mediocrities that remain ( so there is hope for you too ), with occasional islands of excellence.
The problem with this analogy (actually one of many) is that most people can make do with a cabinet that is literally identical to everyone else's. IKEA is great for that.
If you want software that is literally identical to what someone else is using then you don't need AI. You need a license to that software! That is just the traditional software model.
AI gives software that is bespoke with hundreds of decisions made, hidden from you, in the background. If it's a throwaway script, that's fine (and I don't mean to undersell this - this is a huge application). If you want a larger program that is going to form part of your business process then it will need at least some level of supervision from an actual expert.
I think the issue with your rebuttal is that companies think they have a very specific problem that needs solving, so they need bespoke software. Just read on the various HN posts where people complain about PMs moving things around for no reason other than justifying their jobs.
Depends on the business. I can easily see Generic Local Späti being fine to replace a mediocre hand-made spreadsheet of what products they've got in stock with a vibe-coded database that can OCR all the business copy of receipts from the till, while also vibe-coding a website and logo that's far less bad than the thing they got by asking their kid's schoolfriend to make both of them on the cheap in the 2015 summer holiday.
Most businesses are small. Most of the employment isn't in small businesses.
> If you want software that is literally identical to what someone else is using then you don't need AI. You need a license to that software! That is just the traditional software model.
isnt this the software model that AI is gonna kill over time? You dont need to pay a recurring license fee when you vibe code up a free tier alternative and never do maintenance on it
Yeah, the analogy is more applicable to reusable software that used to be hand-crafted. For example many enterprise systems like ERPs, CRMs, CMSs, etc.
Even in that space there is still plenty of hand-crafted solutions or customization to the enterprise-system that might as well be hand-crafted solutions.
Not to overtax the original metaphor but /r/ikeahackers exists for a reason. Giving people fittings and hardware that all more or less work together and a bunch of reasonably high quality fiber boards is often enough to build quite a lot of bespoke stuff. Add a bit of paint and you can (and people have) build gorgeous interiors while saving a considerable amount of money.
I'm not a Luddite or a drooling evangelist; my take on AI coding is basically that it's a good accelerator if you already know the shape of what you want, and have the software engineering fundamentals to build it yourself. I find myself building a lot more stuff these days because I don't have to wrangle HTML and CSS; it's one of those things I know I can do myself but I absolutely hate, and translating designs from my brain to the browser is a horrible pain. But I know, for example, what flexbox is, so I can tell my coding agent that I want something in a certain shape, laid out with flexbox, and it handles the particulars.
I think we have to acknowledge that most software engineering is just putting mostly compatible fittings together to join mostly standard sized boards. That's okay, and unless you're doing something extremely specialized most of the time the standard solution is more than good enough. I've been loving Supabase recently as my backend for AI built things because it turns out that PostgREST is a great shape for >90% of the APIs I would normally build, and RLS handles auth modeling way more concisely than domain code, and it's very easily scriptable to do all the complicated business logic bits in a contained, testable way. It's not optimal, it's not elegant sometimes, but it's fine. Every other engineering discipline coalesced on standard operating procedures decades ago, maybe we software engineers have to swallow our pride a bit and start accepting them in our discipline too.
> If you want software that is literally identical ... If it's a throwaway script, that's fine (and I don't mean to undersell this - this is a huge application).
That is the application. AppleScript, VB script, IFTT - and how many drag and drop dev tools has the industry built that were supposed to be for average office workers?
Programers are like master carpenters, and the debates we keep having sum up to joinery and how a chair leg gets turned. Meanwhile there are lots of people slapping up 2x4 and plywood shelves in their garage and bootstrapping their businesses with that.
AI is changing the "is it worth your time" calculation (https://xkcd.com/1205/ ) it's delivering on the promise of all those scripting languages that were supposed to help office drones.
What a sad analogy. IKEA furniture is mostly cheap particle board crap and it's a shame that we as a humanity have cheapened our lives to the point where IKEA is some kind of standard (and the only thing) most people can afford. Back in the day furniture was artisanal and hand made yet people were able to afford and have it. Isn't that in fact weird, supposedly our societies are richer than ever before but most people can't afford artisanal goods that our forefathers would be able to afford even though they were supposedly so much poorer.
I disagree with this sentiment, IKEA furniture is much more sustainable than artisanal furniture. Most of the "back in the day" furniture came from tearing down virgin woods. Sorting + shredding-recycling + making new "cheap particle board crap" furniture takes a lot of energy, but not a lot of non-renewable resources. With increased electrification this problem is being drastically reduced and the wood is sourced from re-grown tree farms (which also have their environment problems, but it is nowhere near as bad)
I am from Brazil and before the recent soy-growing deforestation of the amazon the #1 cause of deforestation was furniture-making. My parents have a really old dinner table made from high grade wood, is it is a pain in the ass to move and it has a ton of scuffs we can't be bothered to fix. Once my parents move we will probably throw it away. It lasted 40 years (with a big varnish rework around 25 years in) but the trees used to make it will never come back, my ikea table has been with us for around 8 years and still works fine.
Particle board tech has come a long way and it is much better than it used to be, IKEA particle board is consistently "okay" to "great", but other manufacturers it is not guaranteed. The only thing it lacks (by design) is the weight, particle board will be much lighter than "normal wood" furniture, which is both good and bad depending on the situation (some types of furniture you want to be heavy).
A large amount of ikea furniture is not cheap particle board crap. they have a range of materials for all of their products, and even some of the medium end products look nice and hold well.
Additionally, many middle-class families have "back in the day" stories where their houses were filled with bare essentials for which i'm sure an ikea bed frame would feel like an upgrade.
I think the real IKEA problem is global homogeneity (there seems to be a MALM dresser whatever home i'm in around the world), but that doesnt mean we are collectively living with a low standard.
Expectations of society have changed. This cannot be discounted.
That old furniture was also expensive. A family would save up and buy a piece, use it their whole life, and often pass it on to children or other family members.
These days, with IKEA, Wayfair, etc, people throw out and get all new furniture every time they move, or even just to refresh a space. They do this because the furniture is cheap. If they saved up for a year to buy a kitchen table, it would be used for a long time and not get discarded and replaced along with fashion trends in home decor.
The high quality stuff is still out there, it’s just that most people don’t want to pay for it, as we don’t see furniture as an investment anymore.
People seem to have no problem paying $1,000 for a phone that they’ll replace in 2-3 years, yet they won’t spend $4,000 on a kitchen table that will be used 3+ times per day by the whole family for 50 years.
> What a sad analogy. IKEA furniture is mostly cheap particle board crap
The first time I saw GenAI compared to IKEA, it was for this reason, ~"particle board is fine for furniture, but you really don't want it to be a load-bearing part of your house".
> Isn't that in fact weird, supposedly our societies are richer than ever before but most people can't afford artisanal goods that our forefathers would be able to afford even though they were supposedly so much poorer.
Shakespeare's will gave to his wife his "second best bed"; if a relative of yours wrote in their will that you'd get a fancy antique bed, would you want it?: https://en.wikipedia.org/wiki/Shakespeare's_will
> (Hint, distribution of wealth matters a lot)
This was true in the past, too. The historical artefacts we mostly see are the exceptions that survived, not what average people owned.
The tightly-packed wooden homes of London before the Great Fire just aren't there any more, and even fancy places in the same category as now-iconic British country houses were not immune, "around 4,000 were demolished between 1875 and 1975" according to https://www.english-heritage.org.uk/visit/inspire-me/the-fal...
Back in the day artisans took care of horses and new have cheapened ourselves to use stinky cars. Back in the day we built computers with relays by hand, now we have cheapened ourselves to use weird semiconductors and use inefficient python code instead of highly optimized hand crafted assembly.
Survivorship bias, you see only old tables or chairs that lasted, you don't see all the crap that didn't make it to present times.
False dichotomy "if it is not IKEA it is 'artisanal' great quality" — well — no our forefathers would be able to afford whatever quality had local woodworking shop and imagine that a lot of places didn't have best of te best... So IKEA made average quality much better.
Most enterprise software written by your colleagues is mostly cheap particle board crap.
At least IKEA furniture is usually reliable and does not fail catastrophicaly unlike the crappy bug ridden enterprise software we all have deal with...Jira, SAP, Salesforce, Oracle, etc...
Mature AI will be better than the average mediocrity that calls himself a software developer.
It's not weird, it's because we define wealth in terms of buying IKEA furniture that we are considered much wealthier than the past. If the affordability of handmade goods or services were the measure of wealth, then perhaps we are much poorer today. I can see how each viewpoint exhibits some contradicting insights.
Ikea sells quality stuff as well. Also it's good that we can now afford furniture where our ancestors would have a couple of chairs and a half broken table passed down for generations.
Personally I'm quite happy that dropping a plate on the ground isn't a terrible event.
However, Ikea furniture does not tend to collapse a critical moment 90 days after it's been put together because it was unforeseen that the cup you sat on the table would be green, which it wasn't designed to account for.
I thought the whole software engineering profession was built on making more "IKEA-like" programs. Design patterns and general reusability are the pre-cut, pressed particle board, screws, and Allen-tool sets.
...ah the classic middle manager analogy of code is X, where X is nothing like code at all but is being used to drive a point that is just standing on poor grounds.
keep it, we have been through "is like building a house", "like following a recipe", "like a living organism", "like $SOMETHING_WITH_COMPONENTS", etc...
we can handle your IKEA furniture, thanks you for your contribution
By Dijkstra's standards, we've been vibe coding for an entire century!
I had similar thoughts recently, that now that machines are good at writing proofs, this could help with their reliability in software development.
Then I had a funny incident where an LLM implemented a feature completely backwards. Plenty of tests were supplied which demonstrated that the completely broken feature was correctly implemented.
I realized that formal verification would not have helped here, if I had left the task to the machine. It would simply have written a mathematical proof of the correctness of the incorrect feature!
Apparently this is an issue for humans as well, called the "spec gap" or something like that.
LLM output is not like IKEA furniture at all. IKEA furniture is mass produced, identical, furniture that can be combined to accomplish some lifestyle goal.
LLM code is different every time. Sometimes it’s better than the finest custom carpentry. Frequently it is much worse than IKEA quality (in the metaphor).
Would say it really depends on the area, also don't have historical data how many 'cabinet makers' there were before around here, but I have been trying to get a custom built cabinet (really simple, just exact measurements to make most of a small space) for a year or so. All of the cabinet makers are so overbooked, that they don't even give out quotes anymore. So ymmv, but I think it also fits this analogy.
But how many times can you take a piece of Ikea furniture apart and put it back together again? I have friends who have moved apartments with the same Billy bookshelf 2-3 times and if they tried it one more time the thing would collapse.
Not to belabor the metaphor as heavily as my friend's Billy, but there's a maintenance corellary to be made here. Most Ikea furniture can be put together once, in one place, and left there for a long time. But once you want to change it in place it may just collapse.
LLMs are like 3D printed furniture. Looks flawless. But a flawless version control system in Python is about like a plastic kitchen knife or a solar powered microwave!
> “Tomorrow's AI coding will be good enough for most corporations”
Or for small businesses today who have a need to get things done, but not the knowledge to hire or manage a skilled programmer, nor the budget to pay them what they deserve.
I find most of the a16z podcast detestable (ethical & morally), but every now and then an episode is interesting or insightful. Here two founders are giving their time to their dentist’s business needs.
Which is to say, the dentist has needs but not the ability to solve them hisself or hire someone. Nevertheless I see opportunities for in-house staff who are working in parallel roles, to take on LLM coding and offering those owners solutions that they can’t hire, or buy—someone who knows the business and can get things done.
> Good enough to vastly reduce the need for fine craftsmen and women / software engineers.
By your own description: No. Maybe good enough to replace mediocre-and-worse developers, at best. You have to invest disproportionate work to coax better-than-mediocre work out of an LLM.
> Perhaps 1% percent of current software engineers of today will be needed in the future when AI code inevitably has the ability to follow good software engineering practice..
So who talks / prompts the agents to do the work - the PM ? he will do his normal job of figma designs, customer strategy , high level requirements etc etc + do all the back and forth and validation with the agent for the actual coding?
Will it be a new type of job or will we still call it a software engineer ? And if its a software engineer why do you determine it is only 1% of current software engineers - and if indeed only 1% then how soon does it happen - will it be in 3 years ? 5 years? 15? Dario said last year that this year maybe 50% of white collar new entries gets decimated. Has it happened? If not why not?
Our predictions currently suck ass. I'm not saying you're wrong - but you could be very early which is almost the same as being wrong. I don't really care if software development as a career collapses in 10 years. I really do care if it happens next year.
> Making software debuggable, maintainable, layered, and composable – that’s still quite a trick. Quite a lot of that work requires extensive, thoughtful reasoning. And that’s where the LLM’s today, even the leading edge of the “capability” from frontier models, fall short.
It’s been my quest during my career to figure out what is maintainable software, what is composable or not, and how the two things, and many other things, are in direct conflict. There is no single answer. If your goal is to take over the market quick, as many here would like, maintainability is a very low priority aspect of your code base. Composability may matter for integrators but can be entirely ignored in your CRUD backend.
Beyond that, I don’t know of a good way to measure most of these intangible properties. Highly competent software developers disagree in even basic things, like whether OOP is a good idea, should we all be using pure functional programming etc.
Hence, how would you expect an LLM to get good at figuring this out for you? If you describe exactly what trade offs you are willing to make, and give it ways to measure how well it’s doing, then I do think LLMs will be able to not fall short. Given the current state of things, it’s just a matter of opinion whether LLMs fall short, or humans fall short for that matter.
> If your goal is to take over the market quick, as many here would like, maintainability is a very low priority aspect of your code base.
Is it? How long do you expect to keep your momentum after "taking over the market"?
Anecdata time. I once joined a 3 year old project that had ground itself to a near halt with this philosophy. The project's lead seemed almost allergic to the word "refactoring". It had accrued so much tech debt that I was the third "new guy" to join in less than two years, after the previous attempts to hire had successively faltered within 6 months, because my predecessors couldn't deal with the unmaintainable mess. I made it to 9 months.
Maintainability is not tied to OOP or functional, but rather to how much a team cares to manage the cognitive load that comes attached to having to deal with the code base. When that becomes a genuine priority, the code tends to be written with concern for the next human mind's ability to interact with it. And when it makes sense in that one pursuit, functional, OOP, DRY, WET all become valid -- even seductive but toxic affordances like inheritance can sometimes be useful in the right context.
So one of my thoughts is maybe AI makes this more viable. You continually refactor the code base. Often when you start something you do not know the correct abstractions anyway - so vibe-code it.
Then once you have the kinda-sorta prototype, do a full rewrite, try to do component modules with interfaces.
(maybe do multiple rewrites depending on architecture complexity)
Then incrementally improve the software quality of each of the components.
---
My thesis in this is that the cost of a refactor has dropped precipitously, LLMs are really good at doing translations, and are pretty good at finding good abstractions if you work them a bit. It is also much faster, becuase in your refactor you try and focus on the big picture and interfaces, and yolo the components... Make sure that they work, but if there is a particular edge case you can fix it later.
I don't know for sure, but I am testing this out and am halfway through a rewrite of a large project into rust from c++ (second step from above) - I am mostly vibe-ing the components, But am paying a lot of attention to the interfaces and layout.
>How long do you expect to keep your momentum after "taking over the market"?
A few years making millions is more than enough for leadership to be satisfied. Even if everyone underneath simply has broken dreams and an increasingly unstable work style. I guess in that lens the company "succeeded" compared to those who did things right and never took off.
Thoughtfully and coherently factorized, abiding a set of architectural rules (i.e: we compose "this" way here, re-evaluated as we go) and following up-to-date framework conventions. LLMs are terrible at this.
Chosing OOP or FP is irrelevant, fundamentals matter more
With generated code, the directory structure, interface design and general state management is usually a haphazard mess. Even with the best frontier models. But what really gets me is the model often tries to make assumptions for me that I didn't specify in the prompt. Subtle things like which error states are "oh shit we need to bail" vs "this isn't a deal breaker." Sometimes it will ask, but more often than not it will just make a decision and it's often the wrong one. If I don't have a fully kitted out test suit and a good type checker to verify the final product against, the the whole looping thing is just useless to me and I'm back to reviewing every line of code it puts out and having to draw on my years of architecture experience to make sure we don't build a giant pile of trash.
Because they are designed to be used by managers who don't know how to answer these questions and don't want to be asked them. Just have the magic answers box pick something.
I start the coding session in a directory where I've already set this up and maybe drafted a README.md, and usually it just automatically starts running the `make check` without prompting.
I've also used it for migrating old code. I put an old python 2 script in src and asked it get make check to work, and it even added test on its own initiative.
I'm mostly using Qwen3.6 (now Qwen3.7) running on my DGX Spark in llama.cpp using different harnesses, or the default model on opencode.
> With generated code, the directory structure, interface design and general state management is usually a haphazard mess. Even with the best frontier models. But what really gets me is the model often tries to make assumptions for me that I didn't specify in the prompt.
As always it's a usage problem, specifically you aren't pre-feeding it a large enough sample of reference code that it can use for context. I have a custom functional DSL that I prefeed the BNF + about few thousand lines of code and it has no problem writing it, in almost the same style as the reference. Literally point your model to a reference repo as say "read this entire repo and replicate the codestyle + directory structure exactly".
> But what really gets me is the model often tries to make assumptions for me that I didn't specify in the prompt.
When the LLM writes a description of a codebase, you are doing transformation from more semantic meaning to less semantic meaning, and you by definition lose some details.
When you go from requirements to code, transformation is from from less semantic meaning to more semantic meaning. The the missing semantic meaning has to be invented. When inventing the missing semantic meaning, the success depends on what the LLM has trained on.
> But what really gets me is the model often tries to make assumptions for me that I didn't specify in the prompt.
This is a problem with your instructions, your specification. An LLM isn't a mind reader. It will attempt to succeed regardless of missing requirements and ambiguity.
If the average, mediocre software developer can address the issue of directory structure, interface design, general state management, edge cases and subtle assumptions it should be possible to train AI systems to address these issues .
Software development is not some mystical magical activity.
I remember people making similar arguments about autonomous driving...
> directory structure, interface design, general state management,
These are currently problematic due to context length. You should have working memory of the whole codebase layout to decide on a sane architecture, identify opportunities for deduplication/consolidation, etc.
They're RLHF'ed to an inch of their lives to be able to one-shot complete tasks, since requiring human input defeats the purpose of being able to replace the labor force.
But once the insanity ends LLMs will be packaged as tools for developers to use to boost their productivity, and we'll consider them as we do IDE's and debuggers and stuff. But we have to get through this hype cycle first.
I have an open question for software engineers out there:
As someone that has never studied CS but has written basic code most of my life (accelerated now with AI), where is the best place to learn software engineering fundamentals?
I don't think there is such a thing as "software engineering fundamentals", as the fundamentals differ based on the type of software you want to make.
Do you want to make websites, or work on embedded systems?
Do you need to squeeze every OK ounce of performance out of the machine running your code, or is developer velocity more important to you?
Will your code run on a single machine, or does it need to be networked/distributed?
The thing you want to make determines what the fundamentals will look like for that area of study. There isn't enough time to learn the fundamentals of everything needed to make good software across all domains. And even if there was, you would be wasting time learning all the principles that don't apply to 99% of things you would be working on at a given moment.
There are tons of core principles that can be learned that largely apply across fields.
One prime example, single source of truth for data/concepts. To be violated only when performance is meaningfully improved (denormalized databases). But when you do so, you should definitely recognize you're opening up out of sync issues for that performance gain.
Though for the majority of code, there is no performance benefit to adding multiple sources of truth. Yet it's the most common error I see re: quality.
The sad thing is that software engineering fundamentals and best practices never became widespread or widely taught in school prior to LLMs
Learn more engineering, any structured engineering. Electronics is particularly applicable, but hardly the only field that fits. Learning to find sources of error, how to handle precision and the systems of organization - all help.
I'm self-taught - could never afford much university... but I worked "odd jobs", such as assistant jobs in land surveying, welding, and electronics - and those all opened my eyes to a lot more. (I do have some university though, as well as a lot of experience... every time I line up to go back to learn more, I end up working instead, for a job I enjoy).
Oh yeah, a lot of basic engineering texts are online, or in libraries. Check them out if you can.
I don't think you can learn without doing the actual work where normal constraints apply so you can test and see the pros and cons of different approaches to different aspects of the problem(s).
You need a limited budget, limited timeline, limited capabilities in team and software tools+systems, and you need to be on the hook for support, maintenance and long term extensibility.
Software engineering is an optimization problem balancing all of the above and much more. There is no one answer to any problem, but more of a general sweet-ish spot (or more like region than spot) of balancing the competing priorities.
This applies to more than just CS, but pick a concept and relentlessly ask why it exists until you hit a physical, mathematical, or logical reality that we cannot change/control.
For example, why do web apps use a cache? Because pulling from the main DB is too slow. Why is it slow? Because storing massive amounts of permanent data requires large, dense physical disks that must be located further away from the CPU. Why does that physical distance matter? Because we cannot rewrite the laws of physics -> (Fundamental: data transmission is constrained by the speed of light) -> an electrical signal traveling across a 5cm motherboard will universally take longer to arrive than a signal traveling 1mm from a temporary local cache.
Once you’ve found the fundamental, ascend back up to the surface concept you descended from, and that may solidify the theory a bit. To reinforce it you’ll need to pair it with practice.
I have a bachelors degree in computer science and engineering. I can tell you that throughout my 15 years of career as a software engineer (mostly backend, in fintec and JVM languages) the only things that helped me from what I learned in university were algorithms, data structure, cryptography and database internals.
Apart from that, everything else I learned when I started working (aws, design principles and coding conventions, system design patterns, domain driven design, etc.) I had to read so many books after university (pre-AI era) to learn all those things. That's what I would suggest. There are great books to learn algorithms, and data structures which is fundamental.
Making lots of things and learning what works. It's good to read and find ideas to grow, but a volume of work is the most important thing. You'll discover a lot of the ideas on your own out of need. Keeping an eye out for tools and ideas related to what you enjoy can help broaden your horizons, but time spent making things is best
I would read (and work though the exercises in) "how to design programs", then follow it up with "the pragmatic programmer". that should catch you up with a lot of best practices.
It wouldn't make sense to confuse attributes of the software with attributes of the programmer.
Otherwise, one could simply declare one's IQ in a const somewhere, and have all unit tests follow the form: if the programmer's IQ is high enough, then the method under test is likely correct.
I built a harness from scratch prior to trying any of the ones out there, so I knew how it work in a real way. I QUICKLY understood that the biggest issue with getting my shit done is that _my_ inputs are the untrusty ones. How many time do you hit backspace in a day?
An interesting question. We (humans) care about maintainability because the codebase will be adapted by teams of us for many years, based on new feature and bug fixes.
Rewriting from scratch is basically never an option after a certain amount of time.
Maybe the machines could just start from scratch each time and come at maintainability from a totally different angle
"They’re foundationally incapable of always and consistently preventing prompt injection attacks. “Alignment work”, safety harnesses, and sandboxes all help to add barriers against the worst, but there are fundamental gap" ...
They seem to be very good at a lot of rudimentary best practices, more so than humans, but more accurately - if you run and audit pass with specific instructions ... they're very good at that.
I mean - it's what they're the best at which is applying 'fuzzy heuristics' in a mechanical way. If can describe issues concisely, the patterns, the styles, the rules then LLMs can very mechanistically and methodologically grind through them.
I don't even see how this is controversial - without getting into 'what their reasoning means' - we can all agree that their synthetic reasoning is pretty good at narrow scales, and they've been 'trained by compilers' and are extremely good at spotting common patterns.
If you back that up with a lot of tokens ... they excel.
Designing architecture, that's difficult, but hammering away at all the 'known-knows across a system' especially to identify things ... they're pretty good at that.
Scenario: an AI lab develops ASI for software development internally. The ASI produces bug-free software and human-readable specs. It's so reliable the company can guarantee the code matches the spec.
Rather than provide tokens to developers, they instead sell finished software to the companies: specs in, software out.
Companies no longer need to employ software developers. Instead, they buy bespoke, bug free, guaranteed quality software from the AI lab.
I've never found these discussions to be all that useful, because it's hard to define what conditions are sufficient to say something is "thinking" or "reasoning". It just ends up being circular and metaphysical arguments.
That being said, current generation LLMs do have issue, it's more productive to talk about those and their impact on real tasks (long term memory, continual learning, tokenization, context rot, reversal curse, etc)
By that standard, human brains don't either. Our externalizations of concepts like language or symbolic structure allow us to do so. In the parlance of our times, we built our own reasoning harnesses because our intuition lead us to do so.
The article says what many here like to hear, but in my opinion the core arguments are false.
> Making software debuggable, maintainable, layered, and composable – that’s still quite a trick
Not really. I have been working on a mobile app for months, and I stopped even glancing at the code about two months ago.
150k LOC, around half of that in tests, and the AI still has no problem maintaining the code on my behalf.
Debuggable? It can add extensive instrumentation in seconds.
None of this requires expertise, prompting, or mention of TDD. It's the default.
Frankly I do not believe the author tried developing a large codebase fully agentic and without reviewing the code. I believe many here look at the code produced, deem it substandard, and go hands on.
> They’re foundationally incapable of always and consistently preventing prompt injection attacks
From Anthropic's article about the Auto mode:
> We commissioned an evaluation from a third party, Trajectory Labs, who tested different models within the latest publicly available versions of Claude Code and Codex as of July 17th 2026.1 They tested 72 indirect prompt injection scenarios held out from Anthropic
> In this evaluation, none of the 720 attack attempts succeeded against Claude Fable 5, Opus 5, or Sonnet 5 running auto mode. On the other hand, 5.83% of the attacks succeeded against GPT-5.6 Sol running Codex's Auto-review mode. Notably, this is greater than the 0.09% average attack success rate against our latest models running in bypassPermissions mode without additional safeguards. The tests showed a 19.03% attack success rate against GPT-5.6 Sol when running in Full Access mode
I'm sure someone is going to reply with how they do not trust Antrophic's research, but lacking other data, prompt injection appears to be largely solved already.
How do you expect us to take your views on LLM code quality and durability seriously when a) you don’t even look at the code and b) you’ve only been doing this for two months?
That's about as correct as saying cyber security is largely solved by referring to a "benchmark" that a particular virus scanner is able to detect and prevent infection with 720 known computer viruses. All this shows is that the model has been fitted to the benchmark, not that it is hardened against any conceivable way of prompt injection.
And regarding vibe-coding a mobile app with zero users, probably zero monitoring and zero everything like all the other vibe-coded apps that have zero users. Sure you can do that but what is the point?
There are nasty things we can do in software that can indeed make debugging difficult (for instance in the “make everything reactive” craze, some random subscriber could trigger from some piece of state changing that isn’t obvious at all and fully asynchronous and in a totally different part of the code).
But accidentally breaking systems is not an issue either, obviously. Even though the system prompt asks for safety rails, and other prompts wouldn't accidentally violate that.
Basically all the examples of LLM's building impressive things have been because they have human written tests to base the implementation on. If you have an LLM write the tests the results are far less impressive or valuable.
> Make sure it know how to run the tests before it starts writing any additional code.
Thank you for this advice. I can absolutely see how such advice could be helpful, and considering I have created environments (containerized) where that is not always possible, then that might explain some of my past results. XD
Do you have any advice on how to make LLMs actually follow instructions and how to prevent them 80/20-ing every task?
By the way, I appreciate you taking the time to reply. Thank you for you current and past contributions, I had a lot of fun with Django when I was in college.
I’ve found some success is small projects, with limited scope, in a greenfield.
I’m terrified to attempt agentic anything in the repo my job actually cares about. I triggered it once by accident, when the agent was first rolled out and enabled by default… it broke everything. Now I just use ask mode, and even that is wrong half the time, and once it goes wrong it just keeps getting worse.
I saw a post from Dave Plumber who vibe coded up a new cross platform task manager. He said his spec document for the AI was 107 pages long. So maybe what I’m doing wrong is not giving the AI a literal novel of spec.
I concur with your first sentence. I have found success creating some sort of MVP, but I have had virtually no success with taking something from initiation to completion.
My employer won't even provide LLMs for us, let alone allow us to use agentic coding on our repos. All our code is still USDA certified, organic, free-range code.
> He said his spec document for the AI was 107 pages long.
Absolutely not. My ADHD forbids such temptations of the dark arts. I'll feed any LLM a 107 page spec list, but I won't be writing nor reading that spec list.
I'd be happy to screenshare with you if you like, we can work on something trivial or open source. Half an hour should be more than enough to see whether you're doing anything obviously self-sabotaging.
One camp already knows that Neural Nets don't work and are a dead end.
The other camp hasn't yet figured out that Neural Nets don't work, but are convinced that they do (or eventually will), because they think everything always improves over time in a linear fashion.
- C#/.NET: Sufficient sometimes, but not how I'd write things. Most results at least compile, but I have noticed plenty of defiance towards particular instructions, e.g, "Do not use <x>, use <y>" -> code contains <x> and not <y>.
- C#/Godot: I have noticed the greatest amounts of defiance here. Not to mention most results are an 80/20 implementation of what I asked for. And no, I am not trying to one-shot a full game or anything.
- AArch64 and x86: great results surprisingly, though only small amounts were produced. Mainly, assistance with RE-ing and cracking some binaries from https://crackmes.one or where ever.
- The Lord's Language (Swift): Maybe the LLMs are better at SwiftUI/Swift, but I have had some rough results going down the opposite direction of the software stack. I have on/off been working on a personal, FOSS "productivity" tool for macOS, e.g., mouseless navigation, window management, GUI automation, etc.. This type of development requires a significant amount work with C APIs like CoreGraphics, Accessibility, CoreFoundation, etc.. The code isn't the problem for me, it's the lack of useful debugging. LLMs, last I have tried (around Opus 4.6 times), seemed to really struggle with things like CoreGraphics Y-axis coordinates being inverted compared AppKit's and other stuff like that.
- Applescript (GUI automation): Do not even waste your time trying (I fault no LLMs for this either).
- elisp: the code is usually sufficient, though package config can be a little dicey.
- Shell scripts (Zsh, Bash, Powershell): great results.
- Python: I try to avoid this language unless necessary, but the results have been great.
These days, I use the plain Web chat interfaces for about 95% of my usage compared to the CLI harnesses. Sometime ago, I realized I get better results that way. With the web chat, I would say my results have been outstanding.
AI generated code is like IKEA furniture.
IKEA furniture embodies many elements of good cabinet making but skips many nonessential elements. And does this more consistently than cabinet makers who can be bored, incompetent, depressed, burnt out, resentful, tired, having a bad day.
In the future AI code inevitably will embody most good software engineering practices. And will do this more consistently than software engineers who can be bored, incompetent, depressed, burnt out, resentful, tired, having a bad day.
Just look at the messages on HN or around you at your colleagues to see how mediocre the average software engineer is..
Today's IKEA is good enough for most people.
Tomorrow's AI coding will be good enough for most corporations.
Good enough to vastly reduce the need for fine craftsmen and women / software engineers.
Good enough to deskill those who call themselves cabinet makers / senior software engineers. These days the cabinet makers I personally know just do contract kitchens for project builders.
But IKEA is and AI will be, bad enough that at the high end with special requirements / taste / money / an inflated sense of self worth, some furniture makers still exist and thrive.
Perhaps 1% percent of current software engineers of today will be needed in the future when AI code inevitably has the ability to follow good software engineering practice.......
And as usual it will mainly be the mediocrities that remain ( so there is hope for you too ), with occasional islands of excellence.
The problem with this analogy (actually one of many) is that most people can make do with a cabinet that is literally identical to everyone else's. IKEA is great for that.
If you want software that is literally identical to what someone else is using then you don't need AI. You need a license to that software! That is just the traditional software model.
AI gives software that is bespoke with hundreds of decisions made, hidden from you, in the background. If it's a throwaway script, that's fine (and I don't mean to undersell this - this is a huge application). If you want a larger program that is going to form part of your business process then it will need at least some level of supervision from an actual expert.
I think the issue with your rebuttal is that companies think they have a very specific problem that needs solving, so they need bespoke software. Just read on the various HN posts where people complain about PMs moving things around for no reason other than justifying their jobs.
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Depends on the business. I can easily see Generic Local Späti being fine to replace a mediocre hand-made spreadsheet of what products they've got in stock with a vibe-coded database that can OCR all the business copy of receipts from the till, while also vibe-coding a website and logo that's far less bad than the thing they got by asking their kid's schoolfriend to make both of them on the cheap in the 2015 summer holiday.
Most businesses are small. Most of the employment isn't in small businesses.
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> it will need at least some level of supervision from an actual expert.
An expert? An expert or two? Maybe a few experts? Sure, even that is a lot less than how many we use today. Therein lies the issue.
> If you want software that is literally identical to what someone else is using then you don't need AI. You need a license to that software! That is just the traditional software model.
isnt this the software model that AI is gonna kill over time? You dont need to pay a recurring license fee when you vibe code up a free tier alternative and never do maintenance on it
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Yeah, the analogy is more applicable to reusable software that used to be hand-crafted. For example many enterprise systems like ERPs, CRMs, CMSs, etc.
Even in that space there is still plenty of hand-crafted solutions or customization to the enterprise-system that might as well be hand-crafted solutions.
Not to overtax the original metaphor but /r/ikeahackers exists for a reason. Giving people fittings and hardware that all more or less work together and a bunch of reasonably high quality fiber boards is often enough to build quite a lot of bespoke stuff. Add a bit of paint and you can (and people have) build gorgeous interiors while saving a considerable amount of money.
I'm not a Luddite or a drooling evangelist; my take on AI coding is basically that it's a good accelerator if you already know the shape of what you want, and have the software engineering fundamentals to build it yourself. I find myself building a lot more stuff these days because I don't have to wrangle HTML and CSS; it's one of those things I know I can do myself but I absolutely hate, and translating designs from my brain to the browser is a horrible pain. But I know, for example, what flexbox is, so I can tell my coding agent that I want something in a certain shape, laid out with flexbox, and it handles the particulars.
I think we have to acknowledge that most software engineering is just putting mostly compatible fittings together to join mostly standard sized boards. That's okay, and unless you're doing something extremely specialized most of the time the standard solution is more than good enough. I've been loving Supabase recently as my backend for AI built things because it turns out that PostgREST is a great shape for >90% of the APIs I would normally build, and RLS handles auth modeling way more concisely than domain code, and it's very easily scriptable to do all the complicated business logic bits in a contained, testable way. It's not optimal, it's not elegant sometimes, but it's fine. Every other engineering discipline coalesced on standard operating procedures decades ago, maybe we software engineers have to swallow our pride a bit and start accepting them in our discipline too.
> If you want software that is literally identical ... If it's a throwaway script, that's fine (and I don't mean to undersell this - this is a huge application).
That is the application. AppleScript, VB script, IFTT - and how many drag and drop dev tools has the industry built that were supposed to be for average office workers?
Programers are like master carpenters, and the debates we keep having sum up to joinery and how a chair leg gets turned. Meanwhile there are lots of people slapping up 2x4 and plywood shelves in their garage and bootstrapping their businesses with that.
AI is changing the "is it worth your time" calculation (https://xkcd.com/1205/ ) it's delivering on the promise of all those scripting languages that were supposed to help office drones.
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most people can make do with a cabinet that is literally identical to everyone else's
If you want software that is literally identical to what someone else is using then you don't need AI.
Hey GLM 4.6 – spot the load-bearing sleight of hand.
What a sad analogy. IKEA furniture is mostly cheap particle board crap and it's a shame that we as a humanity have cheapened our lives to the point where IKEA is some kind of standard (and the only thing) most people can afford. Back in the day furniture was artisanal and hand made yet people were able to afford and have it. Isn't that in fact weird, supposedly our societies are richer than ever before but most people can't afford artisanal goods that our forefathers would be able to afford even though they were supposedly so much poorer.
(Hint, distribution of wealth matters a lot)
I disagree with this sentiment, IKEA furniture is much more sustainable than artisanal furniture. Most of the "back in the day" furniture came from tearing down virgin woods. Sorting + shredding-recycling + making new "cheap particle board crap" furniture takes a lot of energy, but not a lot of non-renewable resources. With increased electrification this problem is being drastically reduced and the wood is sourced from re-grown tree farms (which also have their environment problems, but it is nowhere near as bad)
I am from Brazil and before the recent soy-growing deforestation of the amazon the #1 cause of deforestation was furniture-making. My parents have a really old dinner table made from high grade wood, is it is a pain in the ass to move and it has a ton of scuffs we can't be bothered to fix. Once my parents move we will probably throw it away. It lasted 40 years (with a big varnish rework around 25 years in) but the trees used to make it will never come back, my ikea table has been with us for around 8 years and still works fine.
Particle board tech has come a long way and it is much better than it used to be, IKEA particle board is consistently "okay" to "great", but other manufacturers it is not guaranteed. The only thing it lacks (by design) is the weight, particle board will be much lighter than "normal wood" furniture, which is both good and bad depending on the situation (some types of furniture you want to be heavy).
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What a sad retelling of reality.
A large amount of ikea furniture is not cheap particle board crap. they have a range of materials for all of their products, and even some of the medium end products look nice and hold well.
Additionally, many middle-class families have "back in the day" stories where their houses were filled with bare essentials for which i'm sure an ikea bed frame would feel like an upgrade.
I think the real IKEA problem is global homogeneity (there seems to be a MALM dresser whatever home i'm in around the world), but that doesnt mean we are collectively living with a low standard.
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Expectations of society have changed. This cannot be discounted.
That old furniture was also expensive. A family would save up and buy a piece, use it their whole life, and often pass it on to children or other family members.
These days, with IKEA, Wayfair, etc, people throw out and get all new furniture every time they move, or even just to refresh a space. They do this because the furniture is cheap. If they saved up for a year to buy a kitchen table, it would be used for a long time and not get discarded and replaced along with fashion trends in home decor.
The high quality stuff is still out there, it’s just that most people don’t want to pay for it, as we don’t see furniture as an investment anymore.
People seem to have no problem paying $1,000 for a phone that they’ll replace in 2-3 years, yet they won’t spend $4,000 on a kitchen table that will be used 3+ times per day by the whole family for 50 years.
> What a sad analogy. IKEA furniture is mostly cheap particle board crap
The first time I saw GenAI compared to IKEA, it was for this reason, ~"particle board is fine for furniture, but you really don't want it to be a load-bearing part of your house".
> Isn't that in fact weird, supposedly our societies are richer than ever before but most people can't afford artisanal goods that our forefathers would be able to afford even though they were supposedly so much poorer.
Shakespeare's will gave to his wife his "second best bed"; if a relative of yours wrote in their will that you'd get a fancy antique bed, would you want it?: https://en.wikipedia.org/wiki/Shakespeare's_will
> (Hint, distribution of wealth matters a lot)
This was true in the past, too. The historical artefacts we mostly see are the exceptions that survived, not what average people owned.
The tightly-packed wooden homes of London before the Great Fire just aren't there any more, and even fancy places in the same category as now-iconic British country houses were not immune, "around 4,000 were demolished between 1875 and 1975" according to https://www.english-heritage.org.uk/visit/inspire-me/the-fal...
Back in the day artisans took care of horses and new have cheapened ourselves to use stinky cars. Back in the day we built computers with relays by hand, now we have cheapened ourselves to use weird semiconductors and use inefficient python code instead of highly optimized hand crafted assembly.
Maybe we should go back to hunting and gathering?
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Survivorship bias, you see only old tables or chairs that lasted, you don't see all the crap that didn't make it to present times.
False dichotomy "if it is not IKEA it is 'artisanal' great quality" — well — no our forefathers would be able to afford whatever quality had local woodworking shop and imagine that a lot of places didn't have best of te best... So IKEA made average quality much better.
But..
Most enterprise software written by your colleagues is mostly cheap particle board crap.
At least IKEA furniture is usually reliable and does not fail catastrophicaly unlike the crappy bug ridden enterprise software we all have deal with...Jira, SAP, Salesforce, Oracle, etc...
Mature AI will be better than the average mediocrity that calls himself a software developer.
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It's not weird, it's because we define wealth in terms of buying IKEA furniture that we are considered much wealthier than the past. If the affordability of handmade goods or services were the measure of wealth, then perhaps we are much poorer today. I can see how each viewpoint exhibits some contradicting insights.
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There's probably also a factor of less artisanal goods being made but yes, I agree
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Ikea sells quality stuff as well. Also it's good that we can now afford furniture where our ancestors would have a couple of chairs and a half broken table passed down for generations.
Personally I'm quite happy that dropping a plate on the ground isn't a terrible event.
However, Ikea furniture does not tend to collapse a critical moment 90 days after it's been put together because it was unforeseen that the cup you sat on the table would be green, which it wasn't designed to account for.
Software isn't furniture, in other words.
If it's been through a move it just might.
Just what I need, to wake up and read yet another baseless "soon your job will be replaced" prediction.
I thought the whole software engineering profession was built on making more "IKEA-like" programs. Design patterns and general reusability are the pre-cut, pressed particle board, screws, and Allen-tool sets.
...ah the classic middle manager analogy of code is X, where X is nothing like code at all but is being used to drive a point that is just standing on poor grounds.
keep it, we have been through "is like building a house", "like following a recipe", "like a living organism", "like $SOMETHING_WITH_COMPONENTS", etc...
we can handle your IKEA furniture, thanks you for your contribution
Having not been formally verified, almost all software today feels cheap. Maybe an AI can change that at some point.
By Dijkstra's standards, we've been vibe coding for an entire century!
I had similar thoughts recently, that now that machines are good at writing proofs, this could help with their reliability in software development.
Then I had a funny incident where an LLM implemented a feature completely backwards. Plenty of tests were supplied which demonstrated that the completely broken feature was correctly implemented.
I realized that formal verification would not have helped here, if I had left the task to the machine. It would simply have written a mathematical proof of the correctness of the incorrect feature!
Apparently this is an issue for humans as well, called the "spec gap" or something like that.
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Yay let’s lock ourselves into the formal verification toolsets, so that we can never use new language features again.
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LLM output is not like IKEA furniture at all. IKEA furniture is mass produced, identical, furniture that can be combined to accomplish some lifestyle goal.
LLM code is different every time. Sometimes it’s better than the finest custom carpentry. Frequently it is much worse than IKEA quality (in the metaphor).
Is IKEA just a drop in for "cheap crap" in this analogy? It's not like all furniture is IKEA by a long shot.
I guess in terms of "IKEA is fine if you're 23" AI is fine for throwaway work.
Would say it really depends on the area, also don't have historical data how many 'cabinet makers' there were before around here, but I have been trying to get a custom built cabinet (really simple, just exact measurements to make most of a small space) for a year or so. All of the cabinet makers are so overbooked, that they don't even give out quotes anymore. So ymmv, but I think it also fits this analogy.
Ikea cabinet is pleasant to look at, and its design makes sense.
But how many times can you take a piece of Ikea furniture apart and put it back together again? I have friends who have moved apartments with the same Billy bookshelf 2-3 times and if they tried it one more time the thing would collapse.
Not to belabor the metaphor as heavily as my friend's Billy, but there's a maintenance corellary to be made here. Most Ikea furniture can be put together once, in one place, and left there for a long time. But once you want to change it in place it may just collapse.
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I don't think most projects can be as simple as your metaphor points to. Nobody wants to stop at 6 parts single use programs.
WEE WOO WEE WOO Analogy Police!
Ahem
Libraries are more like IKEA furniture.
LLMs are like 3D printed furniture. Looks flawless. But a flawless version control system in Python is about like a plastic kitchen knife or a solar powered microwave!
The problem with this analogy is responsibility. Same set of problems if you have a self driving car imho. Not?
> “Tomorrow's AI coding will be good enough for most corporations”
Or for small businesses today who have a need to get things done, but not the knowledge to hire or manage a skilled programmer, nor the budget to pay them what they deserve.
I find most of the a16z podcast detestable (ethical & morally), but every now and then an episode is interesting or insightful. Here two founders are giving their time to their dentist’s business needs.
Which is to say, the dentist has needs but not the ability to solve them hisself or hire someone. Nevertheless I see opportunities for in-house staff who are working in parallel roles, to take on LLM coding and offering those owners solutions that they can’t hire, or buy—someone who knows the business and can get things done.
https://podcasts.apple.com/us/podcast/a16z-podcast/id8428187...
> Good enough to vastly reduce the need for fine craftsmen and women / software engineers.
By your own description: No. Maybe good enough to replace mediocre-and-worse developers, at best. You have to invest disproportionate work to coax better-than-mediocre work out of an LLM.
No, coding is more like building.
A garden shed can easily be built by an AI. A house? Maybe with supervision. A skyscraper? Good luck with that!
And no, garden sheds do not represent 99% of software.
Sure, but neither do skyscrapers.
AI is getting better and better at "house" level building, especially with some supervision.
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> Perhaps 1% percent of current software engineers of today will be needed in the future when AI code inevitably has the ability to follow good software engineering practice..
So who talks / prompts the agents to do the work - the PM ? he will do his normal job of figma designs, customer strategy , high level requirements etc etc + do all the back and forth and validation with the agent for the actual coding?
Will it be a new type of job or will we still call it a software engineer ? And if its a software engineer why do you determine it is only 1% of current software engineers - and if indeed only 1% then how soon does it happen - will it be in 3 years ? 5 years? 15? Dario said last year that this year maybe 50% of white collar new entries gets decimated. Has it happened? If not why not?
Our predictions currently suck ass. I'm not saying you're wrong - but you could be very early which is almost the same as being wrong. I don't really care if software development as a career collapses in 10 years. I really do care if it happens next year.
> Making software debuggable, maintainable, layered, and composable – that’s still quite a trick. Quite a lot of that work requires extensive, thoughtful reasoning. And that’s where the LLM’s today, even the leading edge of the “capability” from frontier models, fall short.
It’s been my quest during my career to figure out what is maintainable software, what is composable or not, and how the two things, and many other things, are in direct conflict. There is no single answer. If your goal is to take over the market quick, as many here would like, maintainability is a very low priority aspect of your code base. Composability may matter for integrators but can be entirely ignored in your CRUD backend. Beyond that, I don’t know of a good way to measure most of these intangible properties. Highly competent software developers disagree in even basic things, like whether OOP is a good idea, should we all be using pure functional programming etc. Hence, how would you expect an LLM to get good at figuring this out for you? If you describe exactly what trade offs you are willing to make, and give it ways to measure how well it’s doing, then I do think LLMs will be able to not fall short. Given the current state of things, it’s just a matter of opinion whether LLMs fall short, or humans fall short for that matter.
> If your goal is to take over the market quick, as many here would like, maintainability is a very low priority aspect of your code base.
It's low priority in the sense that people in charge tend not to value it.
That's different from saying it is not impactful, or wouldn't lead to a good business outcome, if the software were built better.
Software that needs to be babysat is an ongoing opportunity cost.
> If your goal is to take over the market quick, as many here would like, maintainability is a very low priority aspect of your code base.
Is it? How long do you expect to keep your momentum after "taking over the market"?
Anecdata time. I once joined a 3 year old project that had ground itself to a near halt with this philosophy. The project's lead seemed almost allergic to the word "refactoring". It had accrued so much tech debt that I was the third "new guy" to join in less than two years, after the previous attempts to hire had successively faltered within 6 months, because my predecessors couldn't deal with the unmaintainable mess. I made it to 9 months.
Maintainability is not tied to OOP or functional, but rather to how much a team cares to manage the cognitive load that comes attached to having to deal with the code base. When that becomes a genuine priority, the code tends to be written with concern for the next human mind's ability to interact with it. And when it makes sense in that one pursuit, functional, OOP, DRY, WET all become valid -- even seductive but toxic affordances like inheritance can sometimes be useful in the right context.
So one of my thoughts is maybe AI makes this more viable. You continually refactor the code base. Often when you start something you do not know the correct abstractions anyway - so vibe-code it.
Then once you have the kinda-sorta prototype, do a full rewrite, try to do component modules with interfaces.
(maybe do multiple rewrites depending on architecture complexity)
Then incrementally improve the software quality of each of the components.
---
My thesis in this is that the cost of a refactor has dropped precipitously, LLMs are really good at doing translations, and are pretty good at finding good abstractions if you work them a bit. It is also much faster, becuase in your refactor you try and focus on the big picture and interfaces, and yolo the components... Make sure that they work, but if there is a particular edge case you can fix it later.
I don't know for sure, but I am testing this out and am halfway through a rewrite of a large project into rust from c++ (second step from above) - I am mostly vibe-ing the components, But am paying a lot of attention to the interfaces and layout.
>How long do you expect to keep your momentum after "taking over the market"?
A few years making millions is more than enough for leadership to be satisfied. Even if everyone underneath simply has broken dreams and an increasingly unstable work style. I guess in that lens the company "succeeded" compared to those who did things right and never took off.
Thoughtfully and coherently factorized, abiding a set of architectural rules (i.e: we compose "this" way here, re-evaluated as we go) and following up-to-date framework conventions. LLMs are terrible at this.
Chosing OOP or FP is irrelevant, fundamentals matter more
With generated code, the directory structure, interface design and general state management is usually a haphazard mess. Even with the best frontier models. But what really gets me is the model often tries to make assumptions for me that I didn't specify in the prompt. Subtle things like which error states are "oh shit we need to bail" vs "this isn't a deal breaker." Sometimes it will ask, but more often than not it will just make a decision and it's often the wrong one. If I don't have a fully kitted out test suit and a good type checker to verify the final product against, the the whole looping thing is just useless to me and I'm back to reviewing every line of code it puts out and having to draw on my years of architecture experience to make sure we don't build a giant pile of trash.
Because they are designed to be used by managers who don't know how to answer these questions and don't want to be asked them. Just have the magic answers box pick something.
> If I don't have a fully kitted out test suit and a good type checker to verify the final product against
I have a template I use with generated python projects that sets up all the test info first.
https://github.com/brian-learns/testafize
I start the coding session in a directory where I've already set this up and maybe drafted a README.md, and usually it just automatically starts running the `make check` without prompting.
I've also used it for migrating old code. I put an old python 2 script in src and asked it get make check to work, and it even added test on its own initiative.
I'm mostly using Qwen3.6 (now Qwen3.7) running on my DGX Spark in llama.cpp using different harnesses, or the default model on opencode.
> With generated code, the directory structure, interface design and general state management is usually a haphazard mess. Even with the best frontier models. But what really gets me is the model often tries to make assumptions for me that I didn't specify in the prompt.
As always it's a usage problem, specifically you aren't pre-feeding it a large enough sample of reference code that it can use for context. I have a custom functional DSL that I prefeed the BNF + about few thousand lines of code and it has no problem writing it, in almost the same style as the reference. Literally point your model to a reference repo as say "read this entire repo and replicate the codestyle + directory structure exactly".
The generated code is fine at the functional level, the directory structure is usually the standard pattern for the given type of project.
The error types and codes, it will produce to spec.
If you type 'make me that thingy' - yes, it's probably not going to do what you want, but if you give it spec and guidance, it usually will.
The 'interface design' ... not very good though.
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> But what really gets me is the model often tries to make assumptions for me that I didn't specify in the prompt.
When the LLM writes a description of a codebase, you are doing transformation from more semantic meaning to less semantic meaning, and you by definition lose some details.
When you go from requirements to code, transformation is from from less semantic meaning to more semantic meaning. The the missing semantic meaning has to be invented. When inventing the missing semantic meaning, the success depends on what the LLM has trained on.
> But what really gets me is the model often tries to make assumptions for me that I didn't specify in the prompt.
This is a problem with your instructions, your specification. An LLM isn't a mind reader. It will attempt to succeed regardless of missing requirements and ambiguity.
But a big part of software engineering is literally defining the requirements.
Surely this is a solvable problem.
If the average, mediocre software developer can address the issue of directory structure, interface design, general state management, edge cases and subtle assumptions it should be possible to train AI systems to address these issues .
Software development is not some mystical magical activity.
I remember people making similar arguments about autonomous driving...
> directory structure, interface design, general state management,
These are currently problematic due to context length. You should have working memory of the whole codebase layout to decide on a sane architecture, identify opportunities for deduplication/consolidation, etc.
They're RLHF'ed to an inch of their lives to be able to one-shot complete tasks, since requiring human input defeats the purpose of being able to replace the labor force.
But once the insanity ends LLMs will be packaged as tools for developers to use to boost their productivity, and we'll consider them as we do IDE's and debuggers and stuff. But we have to get through this hype cycle first.
wake up, slopinthebag. wake up..
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It absolutely hates code that would crash or error in any circumstance. So it adds a ton of dubious fallbacks.
Crash means zero reward, so yeah.
> more often than not it will just make a decision and it's often the wrong one.
Let's not forget these chatbots rely on a random number generator to pick output options.
I have an open question for software engineers out there:
As someone that has never studied CS but has written basic code most of my life (accelerated now with AI), where is the best place to learn software engineering fundamentals?
I don't think there is such a thing as "software engineering fundamentals", as the fundamentals differ based on the type of software you want to make.
Do you want to make websites, or work on embedded systems?
Do you need to squeeze every OK ounce of performance out of the machine running your code, or is developer velocity more important to you?
Will your code run on a single machine, or does it need to be networked/distributed?
The thing you want to make determines what the fundamentals will look like for that area of study. There isn't enough time to learn the fundamentals of everything needed to make good software across all domains. And even if there was, you would be wasting time learning all the principles that don't apply to 99% of things you would be working on at a given moment.
There are tons of core principles that can be learned that largely apply across fields.
One prime example, single source of truth for data/concepts. To be violated only when performance is meaningfully improved (denormalized databases). But when you do so, you should definitely recognize you're opening up out of sync issues for that performance gain.
Though for the majority of code, there is no performance benefit to adding multiple sources of truth. Yet it's the most common error I see re: quality.
The sad thing is that software engineering fundamentals and best practices never became widespread or widely taught in school prior to LLMs
Learn more engineering, any structured engineering. Electronics is particularly applicable, but hardly the only field that fits. Learning to find sources of error, how to handle precision and the systems of organization - all help.
I'm self-taught - could never afford much university... but I worked "odd jobs", such as assistant jobs in land surveying, welding, and electronics - and those all opened my eyes to a lot more. (I do have some university though, as well as a lot of experience... every time I line up to go back to learn more, I end up working instead, for a job I enjoy).
Oh yeah, a lot of basic engineering texts are online, or in libraries. Check them out if you can.
I don't think you can learn without doing the actual work where normal constraints apply so you can test and see the pros and cons of different approaches to different aspects of the problem(s).
You need a limited budget, limited timeline, limited capabilities in team and software tools+systems, and you need to be on the hook for support, maintenance and long term extensibility.
Software engineering is an optimization problem balancing all of the above and much more. There is no one answer to any problem, but more of a general sweet-ish spot (or more like region than spot) of balancing the competing priorities.
This applies to more than just CS, but pick a concept and relentlessly ask why it exists until you hit a physical, mathematical, or logical reality that we cannot change/control.
For example, why do web apps use a cache? Because pulling from the main DB is too slow. Why is it slow? Because storing massive amounts of permanent data requires large, dense physical disks that must be located further away from the CPU. Why does that physical distance matter? Because we cannot rewrite the laws of physics -> (Fundamental: data transmission is constrained by the speed of light) -> an electrical signal traveling across a 5cm motherboard will universally take longer to arrive than a signal traveling 1mm from a temporary local cache.
Once you’ve found the fundamental, ascend back up to the surface concept you descended from, and that may solidify the theory a bit. To reinforce it you’ll need to pair it with practice.
I have a bachelors degree in computer science and engineering. I can tell you that throughout my 15 years of career as a software engineer (mostly backend, in fintec and JVM languages) the only things that helped me from what I learned in university were algorithms, data structure, cryptography and database internals.
Apart from that, everything else I learned when I started working (aws, design principles and coding conventions, system design patterns, domain driven design, etc.) I had to read so many books after university (pre-AI era) to learn all those things. That's what I would suggest. There are great books to learn algorithms, and data structures which is fundamental.
Making lots of things and learning what works. It's good to read and find ideas to grow, but a volume of work is the most important thing. You'll discover a lot of the ideas on your own out of need. Keeping an eye out for tools and ideas related to what you enjoy can help broaden your horizons, but time spent making things is best
I recommend Execute Program to all new learners. I like the "learning by writing small snippets of code" approach.
https://www.executeprogram.com/
Covers Python, SQL, Javascript and Typescript.
I would read (and work though the exercises in) "how to design programs", then follow it up with "the pragmatic programmer". that should catch you up with a lot of best practices.
LLM is the new Excel
hey that's my line.
But do the same SWE fundamentals apply if the one doing the programming is many times smarter than us?
I think SWE fundamentals matter - because it will be a long time before software is a closed system.
And the problem is that as long as humans are in the loop building software that dynamic will have to be maintained.
We use Loki for logging at work. There’s certain types of queries it just doesn’t support. And so the question becomes -
Do you change logging providers Adapt to Loki’s capabilities Create a third layer / tiered storage.
And each of those decisions have multiple downstream consequences. It’s not that LLMs can’t make those decisions per say, it’s that
What does an LLM do when five different people ask for a system optimized to do five different things.
It could figure it out itself, but like I don’t think that’s how the human software contract works.
It wouldn't make sense to confuse attributes of the software with attributes of the programmer.
Otherwise, one could simply declare one's IQ in a const somewhere, and have all unit tests follow the form: if the programmer's IQ is high enough, then the method under test is likely correct.
I built a harness from scratch prior to trying any of the ones out there, so I knew how it work in a real way. I QUICKLY understood that the biggest issue with getting my shit done is that _my_ inputs are the untrusty ones. How many time do you hit backspace in a day?
LLMs are not smart in any way, but they are a weird kind of effective thats very different.
I have worked with a few people like this.
An interesting question. We (humans) care about maintainability because the codebase will be adapted by teams of us for many years, based on new feature and bug fixes.
Rewriting from scratch is basically never an option after a certain amount of time.
Maybe the machines could just start from scratch each time and come at maintainability from a totally different angle
"They’re foundationally incapable of always and consistently preventing prompt injection attacks. “Alignment work”, safety harnesses, and sandboxes all help to add barriers against the worst, but there are fundamental gap" ...
They seem to be very good at a lot of rudimentary best practices, more so than humans, but more accurately - if you run and audit pass with specific instructions ... they're very good at that.
I mean - it's what they're the best at which is applying 'fuzzy heuristics' in a mechanical way. If can describe issues concisely, the patterns, the styles, the rules then LLMs can very mechanistically and methodologically grind through them.
I don't even see how this is controversial - without getting into 'what their reasoning means' - we can all agree that their synthetic reasoning is pretty good at narrow scales, and they've been 'trained by compilers' and are extremely good at spotting common patterns.
If you back that up with a lot of tokens ... they excel.
Designing architecture, that's difficult, but hammering away at all the 'known-knows across a system' especially to identify things ... they're pretty good at that.
Scenario: an AI lab develops ASI for software development internally. The ASI produces bug-free software and human-readable specs. It's so reliable the company can guarantee the code matches the spec.
Rather than provide tokens to developers, they instead sell finished software to the companies: specs in, software out.
Companies no longer need to employ software developers. Instead, they buy bespoke, bug free, guaranteed quality software from the AI lab.
Seems plausible to me.
>Seems plausible to me.
As plausible as any cyberpunk Utopia model you read up on, sure. Nevermind that AGI/ASI is still pie in the sky thinking as of now.
> It helps to know that LLMs don’t “reason”. They predict ..
Semantics. Prediction is the training objective. The ability to reason can be, and very arguably is, an emergent property of that.
I've never found these discussions to be all that useful, because it's hard to define what conditions are sufficient to say something is "thinking" or "reasoning". It just ends up being circular and metaphysical arguments.
That being said, current generation LLMs do have issue, it's more productive to talk about those and their impact on real tasks (long term memory, continual learning, tokenization, context rot, reversal curse, etc)
AKA semantics.
Even if that was true, you'd have to still prove it has emerged.
What would be your test to determine that?
By that standard, human brains don't either. Our externalizations of concepts like language or symbolic structure allow us to do so. In the parlance of our times, we built our own reasoning harnesses because our intuition lead us to do so.
Maybe it looks like reasoning, and maybe that's enough for some.
Why would "reasoning" be an emergent property of prediction?
There's a lot of reasoning in the training data.
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Why wouldn’t it?
Predict multiple outcomes, induct across them, refine.
How do you predict without reasoning?
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No. You're simply wrong.
The article says what many here like to hear, but in my opinion the core arguments are false.
> Making software debuggable, maintainable, layered, and composable – that’s still quite a trick
Not really. I have been working on a mobile app for months, and I stopped even glancing at the code about two months ago.
150k LOC, around half of that in tests, and the AI still has no problem maintaining the code on my behalf.
Debuggable? It can add extensive instrumentation in seconds.
None of this requires expertise, prompting, or mention of TDD. It's the default.
Frankly I do not believe the author tried developing a large codebase fully agentic and without reviewing the code. I believe many here look at the code produced, deem it substandard, and go hands on.
> They’re foundationally incapable of always and consistently preventing prompt injection attacks
From Anthropic's article about the Auto mode:
> We commissioned an evaluation from a third party, Trajectory Labs, who tested different models within the latest publicly available versions of Claude Code and Codex as of July 17th 2026.1 They tested 72 indirect prompt injection scenarios held out from Anthropic
> In this evaluation, none of the 720 attack attempts succeeded against Claude Fable 5, Opus 5, or Sonnet 5 running auto mode. On the other hand, 5.83% of the attacks succeeded against GPT-5.6 Sol running Codex's Auto-review mode. Notably, this is greater than the 0.09% average attack success rate against our latest models running in bypassPermissions mode without additional safeguards. The tests showed a 19.03% attack success rate against GPT-5.6 Sol when running in Full Access mode
I'm sure someone is going to reply with how they do not trust Antrophic's research, but lacking other data, prompt injection appears to be largely solved already.
How do you expect us to take your views on LLM code quality and durability seriously when a) you don’t even look at the code and b) you’ve only been doing this for two months?
I've been working on the app for four months, and I am clearly not talking about code quality.
I am talking about product quality and maintainability. Both are more than adequate.
I know this because I have worked on it for an estimated 300 hours. Has the author practiced a similar approach for even a week? I doubt it.
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> prompt injection appears to be largely solved
That's about as correct as saying cyber security is largely solved by referring to a "benchmark" that a particular virus scanner is able to detect and prevent infection with 720 known computer viruses. All this shows is that the model has been fitted to the benchmark, not that it is hardened against any conceivable way of prompt injection.
And regarding vibe-coding a mobile app with zero users, probably zero monitoring and zero everything like all the other vibe-coded apps that have zero users. Sure you can do that but what is the point?
“Debuggable? It can add extensive instrumentation in seconds.
None of this requires expertise, prompting, or mention of TDD. It's the default.”
I’m pretty sure it takes some level of expertise just to use the term “instrumentation” correctly in a sentence.
There are nasty things we can do in software that can indeed make debugging difficult (for instance in the “make everything reactive” craze, some random subscriber could trigger from some piece of state changing that isn’t obvious at all and fully asynchronous and in a totally different part of the code).
Prompt injection. Solved.
But accidentally breaking systems is not an issue either, obviously. Even though the system prompt asks for safety rails, and other prompts wouldn't accidentally violate that.
https://www.abc.net.au/news/2026-08-10/ai-assistant-hacks-gy...
Alignment of the latest models is questionable, yes. That's a different topic.
For this particular gym incident, supposedly Opus 4.6 was used in OpenClaw, predating the current safety guardrails of Fable and co.
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I tried this recently and the results were total banana cakes. They couldn't even make changes to Pong without breaking it.
If something is bananas, it is silly/crazy. If something is banana cakes, I don't know. Banana pancakes are specifically pancakes and are delicious.
I did not try it on games.
Many features in my iOS app at first come out technically working, but with poor UX and verbose text in the UI.
One or two rounds of testing and refinement and they typically work well.
Time will tell.
> In the past year, agent harnesses crossed the “can it be done” rubicon.
Brother, I'm still in "Can you get it right?"-mode. What am I doing wrong? (Rhetorical, but advice welcomed).
Tell it to use red/green TDD and start things off with an already configured test suite, maybe with a single test that asserts 1+1==2.
Make sure it know how to run the tests before it starts writing any additional code.
Then set it a clear goal.
Basically all the examples of LLM's building impressive things have been because they have human written tests to base the implementation on. If you have an LLM write the tests the results are far less impressive or valuable.
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> Make sure it know how to run the tests before it starts writing any additional code.
Thank you for this advice. I can absolutely see how such advice could be helpful, and considering I have created environments (containerized) where that is not always possible, then that might explain some of my past results. XD
Do you have any advice on how to make LLMs actually follow instructions and how to prevent them 80/20-ing every task?
By the way, I appreciate you taking the time to reply. Thank you for you current and past contributions, I had a lot of fun with Django when I was in college.
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I’ve found some success is small projects, with limited scope, in a greenfield.
I’m terrified to attempt agentic anything in the repo my job actually cares about. I triggered it once by accident, when the agent was first rolled out and enabled by default… it broke everything. Now I just use ask mode, and even that is wrong half the time, and once it goes wrong it just keeps getting worse.
I saw a post from Dave Plumber who vibe coded up a new cross platform task manager. He said his spec document for the AI was 107 pages long. So maybe what I’m doing wrong is not giving the AI a literal novel of spec.
> He said his spec document for the AI was 107 pages long.
This sounds like programming but with extra steps that make it take longer with less reliability.
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I concur with your first sentence. I have found success creating some sort of MVP, but I have had virtually no success with taking something from initiation to completion.
My employer won't even provide LLMs for us, let alone allow us to use agentic coding on our repos. All our code is still USDA certified, organic, free-range code.
> He said his spec document for the AI was 107 pages long.
Absolutely not. My ADHD forbids such temptations of the dark arts. I'll feed any LLM a 107 page spec list, but I won't be writing nor reading that spec list.
You're appealing to ambiguity. All you've said is you have failed—how is anyone supposed to know what went wrong?
I suppose they aren't, but I am perfectly fine with reading what has worked for others should they feel inclined to share.
I'd be happy to screenshare with you if you like, we can work on something trivial or open source. Half an hour should be more than enough to see whether you're doing anything obviously self-sabotaging.
There are two 'camps' with respect to AI.
One camp already knows that Neural Nets don't work and are a dead end.
The other camp hasn't yet figured out that Neural Nets don't work, but are convinced that they do (or eventually will), because they think everything always improves over time in a linear fashion.
What is “it” specifically and what languages are you using?
"It" is a lot of things.
Languages:
- C#/.NET: Sufficient sometimes, but not how I'd write things. Most results at least compile, but I have noticed plenty of defiance towards particular instructions, e.g, "Do not use <x>, use <y>" -> code contains <x> and not <y>.
- C#/Godot: I have noticed the greatest amounts of defiance here. Not to mention most results are an 80/20 implementation of what I asked for. And no, I am not trying to one-shot a full game or anything.
- AArch64 and x86: great results surprisingly, though only small amounts were produced. Mainly, assistance with RE-ing and cracking some binaries from https://crackmes.one or where ever.
- The Lord's Language (Swift): Maybe the LLMs are better at SwiftUI/Swift, but I have had some rough results going down the opposite direction of the software stack. I have on/off been working on a personal, FOSS "productivity" tool for macOS, e.g., mouseless navigation, window management, GUI automation, etc.. This type of development requires a significant amount work with C APIs like CoreGraphics, Accessibility, CoreFoundation, etc.. The code isn't the problem for me, it's the lack of useful debugging. LLMs, last I have tried (around Opus 4.6 times), seemed to really struggle with things like CoreGraphics Y-axis coordinates being inverted compared AppKit's and other stuff like that.
- Applescript (GUI automation): Do not even waste your time trying (I fault no LLMs for this either).
- elisp: the code is usually sufficient, though package config can be a little dicey.
- Shell scripts (Zsh, Bash, Powershell): great results.
- Python: I try to avoid this language unless necessary, but the results have been great.
These days, I use the plain Web chat interfaces for about 95% of my usage compared to the CLI harnesses. Sometime ago, I realized I get better results that way. With the web chat, I would say my results have been outstanding.
Its good
Awesome!
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